Character recognition system and method for shipping containers

ABSTRACT

A system and method, which enables precise identification of characters contained in vehicle license plates, container I.D, chassis I.D, aircraft serial number and other such identification markings. The system can process these identified characters and operate devices, such as access control operations, traffic systems and vehicle and container tracking and management systems, and provide records of all markings together with their images.

This application is a continuation of U.S. patent application Ser. No.:11/573,902, filed Jul. 5, 2005, which claims the benefit of priorityfrom Israeli patent application Ser. No. 162921, filed Jul. 8, 2004, thecontents of both of which are incorporated herein by reference in theirrespective entireties.

FIELD OF THE INVENTION

The present invention relates generally to a system and method forcapturing images and providing automatic characters recognition andprocessing. More specifically, the present invention relates to anefficient multi-functional universal system and method based onautomatic real-time, multi-step identification of characters definingobject I.D.

BACKGROUND OF THE INVENTION

Vehicle traffic and cargo containers transport are undergoing asignificant growth rate worldwide (about 10-15% per year). Currentsecurity supervision run by both private companies and port authoritiesis unable to provide a system that will enable efficient traffic controland monitoring in face of the continuing growth. Automation has a majorrole to play in supporting the efficient handling and capacity requiredfor meeting the growth in container trade.

In accordance with the growing numbers of containers entering ports andcrossing state borders there is a rising demand for a bettersurveillance system to monitor incoming and outgoing traffic. The needis voiced in both the private and public sectors. The need is apparentfor systems that can efficiently and automatically identify various datasuch as license plate numbers, personal identification badges, incomingpackage or mail labels and other various data which appears in numbercode or other form of alphanumeric characters. Automated check andinspection gateways operation of this magnitude and sophistication isstill not utilized in either factories, public companies andorganizations or households today.

Many traffic environments today already employ various levels ofautomation, including sophisticated traffic management systems forvehicular traffic and terminal operating systems (TOS) for containermovement and inventory management.

The automated system described herein include three main Sub-systems:

-   -   1. Image-capturing units, (including illumination devices),    -   2. A software recognition engine, and    -   3. System application programs

The image-capturing units must include an optical and illuminationeffective method able to produce images of a container ID number and/orlicense plate number with sufficient quality, (focus, resolution,contrast, and uniformity), under all operating and ambience conditions,(sunlight, sun glare, night time, adverse weather conditions). Thesoftware recognition engine and application programs must be able toprocess these images and convert them into data for real-timeprocessing.

The hardware and software must operate in unison, and be able to readthe container and vehicle numbers accurately while they are passingthrough a gate lane, being lifted or lowered by a container crane,sitting on a chassis slot or handled by other container handlingequipment. The design and selection of the Image-capturing and softwaresystems has a great impact on the system infrastructure requirements,determining mounting position and constraints for the cameras andilluminators as well as triggering requirements and solutions.

The above applications fail to disclose or teach at least the following:

-   1. How to achieve and decipher complete credible identification of    data received by the system.-   2. These applications approach each application separately and do    not describe one whole operative system which will provide an    effective solution to the need of such a system in a variety of work    places and areas.-   3. How to carry out a system self check process without outside    interference to assess credibility of data analysis.-   4. OCR systems, which these applications are based upon, have    further developed and the demand for an expanded system which will    answer the need of many and be able to carry out a variety of    functions is increasing, these applications do not supply such    answer as needed.

Thus, there is a demonstrated need for a character recognition systemthat is capable of providing accurate and precise identification onsite, unaffected by outside condition such as weather and visibility,and provides reliable and verifiable results.

Furthermore, there is a need for a multi-functional universal systemthat will provide character identification in a wide variety of fieldswith the same success.

Additionally, the system must be able to perform self-testing and dataverification to ensure reliable and repeatable data.

The system architecture must be optimized and designed for OCR. Existingrecognition systems and method are based on high resolution and/or linescan cameras to capture OCR images, as a result these system generatedata that is not always reliable and accurate. In addition these systemsare complex, not easy to operate and great energy consumers.

The system should be based on components, which are modular withcustom-built features for maximum integration, for example lower powerconsumption, and computer-system control.

The system should be able to answer individual needs and demands, andoffer information that is both accurate and easily and cheaplyaccessible.

SUMMARY OF THE INVENTION

Thus the present invention has the following as its objectives, althoughthe following is not exhaustive.

It is an object of the present invention to provide a method and systemfor identifying alphanumeric codes based on multi-level image processingalgorithm. This will enable the most accurate and fast identification ofalpha numeric characters based on simultaneous area scans of images.

It is a further object of the present invention to provide a method andsystem for increased reliability that should achieve highly accurateresults regardless of weather and lighting conditions and will providecredible identification in any place, at hour of the day year round.

It is a further object of the present invention to provide a method andsystem that will be adjustable and easy to tailor to answer the needs ofmany business enterprises and provide a solution for a wide range offacilities where fast, reliable identification is necessary.

It is a further object of the present invention to provide a method andsystem that is both highly credible yet easy to operate and does notrequire expensive hardware and sophisticated computer system to operateunder.

It is a further object of the present invention to provide a method andsystem which is easy to maintain and can operate automatically withoutthe need for human supervision and surveillance.

It is a further objective of the present invention to provide a methodand a system that can read license plate numbers, container I.D marking,chassis I.D marking, aircraft serial numbers etc. After identificationand verification the system should be able to automatically operateconsequent action such as gate opening or closing, alerting relevantsurveillance or security systems and sending identification logs toremote clients.

It is a further objective of the present invention to enable operationof this system in all sea or land ports and important border and accessdestinations without the need for any major changes in the operation andmanagement of these facilities.

Yet a further object of the present invention is to provide a systemthat is easy to assemble and operate, does not require a large space ofoperation and is comprised of a small number of parts and does notrequire much energy for operation.

Still a further object of the present invention is to provide a systemcapable of self-check and data verification, capable of error alert ifsuch occur and with the capability of error fixing and removal. Thesystem should be able to create data bases of relative data and presentthem according to the client specific demands.

These objectives and others not mentioned hereinabove are allaccomplished by the system and method of the present invention, whichcomprises a number of sensors, cameras with synchronized illuminationsystems and a multi-level identification program.

The camera and illumination systems are operated whenever an objectbearing alphanumeric code requiring identification enters the sensorfield. The camera then focuses on the object and images are capturedfrom different angles and zoom positions. Different types of exposuresand different illumination spectrums, are employed in order to achievethe best lighting parameters possible.

The images can be recorded while the object is on the move, up to objectspeeds suitable for the intended applications. The images areimmediately transferred on to the deciphering program capable ofisolating and Identifying the relevant code required. The camera unitcontinues to capture images as required until the code has beendeciphered.

The identified number code (number of strings and images) are displayedon the system's main display, and logged in its local database. Once acode has been recorded it can be used for several applications such asautomatic log recording, in conjunction with a security system to limitaccess or open gates and portals, it can be sent online or usingwireless technology to computer terminals or cell phones as necessary.

The data stored on the system memory and/or media is useful formaintenance, operation review and installation of different applicationssuch as limited access doorways and gates, providing both identificationand log recording of movement within the monitored zone. This type ofinformation management is highly valuable at port terminals and vehicleparking establishments.

BRIEF DESCRIPTION OF THE FIGURES

The following detailed description of exemplary embodiments of thepresent invention can best be understood by reference to theaccompanying drawings, in which:

FIG. 1 is a shows a schematic illustration of a recognition systemaccording to one embodiment of the invention; and

FIG. 2. shows a flow chart detailing the stages of OCRS recognitionprocess in accordance with an exemplary embodiment of the presentinvention;

FIG. 3 depicts a flow chart illustrating the stages of identificationalgorithm according to some embodiment of the present invention; and

In FIG. 4 a-e illustrates different recognition code results accordingto some embodiment of the present invention; and

FIG. 5 illustrates an Image capturing unit according to some embodimentof the present invention; and

FIG. 6 shows a stand-alone Vehicle Access-Control System according tosome embodiment of the present invention; and

FIG. 7 illustrate a Vehicle Access-Control System architecture accordingto some embodiment of the present invention; and

FIG. 8 shows a Searching and Processing System according to someembodiment of the present invention; and

FIG. 9 a shows an ISPS vehicle according to some embodiment of thepresent invention; and

FIG. 9 b shows an exemplary embodiment of single camera ISPS vehicle;and

FIG. 10 a, shows an exemplary embodiment of a container code recognitionsystem; and

FIG. 10 b shows a top view of a camera configuration according to someembodiment of the present invention; and

FIG. 11, an exemplary embodiment of a Quay Crane Recognition System; and

FIG. 12 shows a screen shot of a multi lane plane recognition systemaccording to some embodiment of the present invention; and

FIGS. 13 a-b show two screen shots of an exemplary embodiment of aMonitor module; and

FIG. 14 a, show an exemplary embodiment of a truck and container coderecognition system; and

FIGS. 14 b and 14 c shows a top view of camera configuration accordingto a truck and container code recognition system.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 illustrates different parts of an optical character recognitionsystem (OCRS) 100. OCRS 100 combines hardware and software compartments,providing a fast and highly reliable method of capturing and decipheringtarget images 10. Target image 10, defines a recorded image containingthe characters OCRS is required to decipher and record. The characterscan be any type of alphanumeric code and be placed at different places,such as license plate, chassis body or side of a container.

OCRS 100 is comprised of the following Hardware parts:

Image capturing unit 110—including several cameras 112 the number ofwhich varies according to the specific application (i.e. vehicleaccess-control system and other systems as will be described in FIGS.6-14) The position and operation of the cameras are also adjustabledepending on the specific application. The image capturing unit providesthe system with fast and accurate images to process.

The ability to perform a highly accurate identification is extended asseveral images are captured from different angles and with differentexposure. Each image is processed separately until completeidentification is established. The images undergo adjustment andenhancement to ensure complete reliability, as will be explained indetail in FIG. 3.

Illumination unit 115—includes several means of solid-stateillumination, required to provide sufficient lighting conditions toenable high quality image capture. The position of illuminationsubunits, and the level of illumination provided offer a wide range ofadjustable lighting conditions adequate for different conditions andrequirements. Under this range, target images can be produced in adverseweather and illumination conditions, day or night.

The illumination unit 115 is most important as insufficient lightinggreatly reduces the chances of achieving reliable code identification.

Old fashioned illumination systems are often inadequate, requireexpensive hardware and consume vast amounts of energy. Solid-stateillumination system used in this design was planned to answer specificneeds, and is energy conserving as it only operates under programrequirements and in correspondence with other OCRS units. The specificoperation and integration of the illumination unit will be explained indetail in FIG. 5.

Video servers units 130—for capturing camera 112 images and convert themto data. This unit can also record the data on its internal memory, andtransmit it as compressed data packets over IP. Video servers unitsinclude serial command in/out used for controlling cameras. Some videoservers include high transform link such as USB or camera-link.

Frame grabbers 132—receives video signals from Image capturing unit 110and transmits them, as an array of image pixel bytes to recognitionapplication 160.

The entire process of identification, which usually requires highlysophisticated hardware, can be performed on common PC modules. That isdue to the program use of an algorithm that is relatively of lowcomplexity as will be further detailed below.

I/0 card 120 main purpose is—1) to deliver sensor 125 alerts, to theprocessing units 2) to alert the processing unit when an object entersthe relevant zone 2) to activate predefined illumination level 3)activate gate (as shown in FIG. 6).

Sensor unit 125—includes a number of sensors placed in differentlocation whose:

OCRS 100 employs the following software:

Recognition application 160—an algorithm based program that activatesrecognition process 200. Identification process includes: receivingimages from the video servers units, deciphering the relevant data, andidentify target image 10.

Recognition process 200 is limited to small specific areas within theimage and not the entire capture. This fact highly increase theefficiency as well as time and energy saving. recognition application160 selects areas most likely to contain the target code and recognitionprocess 200 is limited to these areas.

Different types of programs exist tailored to meet specific requirementsand identify several types of objects. For example programs exist suitedto identify vehicle license plates (license identification program),Aircraft tail numbers (plane identification program) etc. a detailedexplanation of Recognition application 160 will be described in FIG. 3.

Management OCRS program (MCP) 170—for controlling OCRS 100. MCP 170manages the OCRS operation and is responsible for saving andtransferring the identified codes to client application.

MCP 170—updates management workstation 190 on target object situation20. For example management workstation 190 can be port workstationreceiving current updates regarding specific crane status.

It should be noted that both software and hardware use, as describedabove, varies in accordance with the demands of each specificidentification system (as will be detailed later on in FIGS. 6-13). Forexample some identification system employ video server units 130 whileothers employ frame grabbers 132.

FIG. 2. shows a flow-chart detailing the stages of OCRS 100 recognitionprocess 200. Recognition process 200 includes 3 phases: Initializationphase 210, image capture phase 220, and post image capture phase 230.

Initialization phase 210 includes two steps;

In the first step updates 212 are made to OCRS 100. These updatesinclude data, which might be useful in the process to follow (forexample car licenses numbers of authorized cars etc.). The update datais transferred and saved by the recognition program.

In the next step, commands 214 are optionally transferred from the videoserial link to the relevant cameras 112 to perform focus, and zoomfunctions on target object 20, and activate illumination unit 115.Commands 214 are delivered upon target object 20 entry to detection zone30. It should be mentioned that commands 214 step, is activated only bysome OCRS 100, for example TCCRS.

Target object 20 is defined as the object, which includes the targetimage 10. A vehicle, for example, is a target object containing alicense plate which is target image 10.

Detection zone 30—defined as the area directly in front of the capturezone 40.

Capture zone 40—defined as the OCRS 100 field of view, within which thecamera devices 112 are capable of detecting and capturing the targetimage 10.

At the end of the initialization phase the system is ready to start theimage capture phase 220.

Entry of target object to capture zone 40 triggers software and hardwareunits, e.g. recognition application 160 and several sensors 125 arecorrespondingly activated.

Each sensor 125 activated, sends a signal via IO card 120 to therecognition application 160. The signals received alert the system tothe object presence within capture zone 40.

Once recognition application 160 receives sensors 125 signals, the imagecapture phase begins. Different cameras 112 take several shots fromdifferent angles and positions and under different illumination levels.The number of images, illumination level, capturing angle, illuminationspectrum and exposure levels are all predetermined according to thespecific target object 20 requiring identification. The number of imagesand illumination levels differ, for example, when taking a picture of acar license plate on a clear day or taking a picture at night.

Post image capture phase 230 begin once target object 20 has leftcapture zone 40, or after a predetermined period set by recognitionapplication 160. Post image capture phase includes the following steps:

-   The images are extracted and sent 232 to recognition application 160    for target code identification.-   Once recognition application 160 has deciphered and identified the    code within each image OCRS 100 operates recognition process 234    that analyzes the results generated for each image, and compare    them. The most accurate result is selected, using identification    algorithm (will be described in detailed in FIG. 4) that compare and    finalizes the data generated.-   At the end of identification process final result=Final Target Code    (FTC) 90 is received.-   A process of logic verification and validation 236 is generated to    verify the reliability of the resulting target code.

FTC 90 is saved and presented by OCRS 100 as DDE (Dynamic Data Exchange)message 95, or alternatively by some other inter-applicationcommunication protocol, such as DCOM, or TCP/IP socket service. DDEmessage 95 is defined as the end result output, which includesinformation as required by specific client 97, such as serial numberrecorded, date and time. Client 97 can save the DDE message or transferthe data to a log file. Additional client application 98 can also usethe message information for other purposes. A windows mechanism, “DDEShare”, can also spread the message through network 99 to remote centralprocessors and databases. Other mechanism are also available to speedthe result, such as transmitting TCP/IP packets with the recognitionresults.

With reference to FIG. 3, a flow chart 300 illustrating the stages ofrecognition algorithm is shown. Recognition application 160 activates amulti-level recognition algorithm for every target image 10, separately.Target image 10 is stored in a string type buffer 315 on which thedeciphering will be carried out. Apart from buffer 315, recognitionapplication 160 includes relevant files, stored in picture init file 320with information and parameters pertaining to recognition process andcommand instructions that describe the format of target image 10.

Once initial image 325 has been stored and buffered the followingoccurs:

-   1. matrix 330 is constructed, based on target image and several    image options according to buffer 315 information and the original    file received 320.-   2 searching 335 candidate areas in target image 10. Candidates areas    are areas in target image with greater probability of containing FTC    90.-   3. Selecting 337 the target area out of the potential areas    pre-selected before.-   4. Adjusting 340 and improving candidate areas which includes    separating characters, in candidates areas from the surrounding    layers and removing other unnecessary noise elements. This process    is carried out by high pass filtering function, and other    algorithms.-   5. Separating 345 selected characters and examining each,    separately.-   6. Identifying 350 and verifying each character utilizing various    control and standards procedures.-   7. transmitting 355 FTC 90 to recognition application database.

With reference to FIG. 4 a-e results of target code identification areshown. As explained before, the success of the identification requiresfor several images to be taken with different capture parameters. Thelogic behind this requirement translates into the following equation:

Error of 3 good images=SQRT of error percentage in each of the imagescaptured. For example if 3 good images are captured, each achieving 90%identification success, for each image there's a 10% chance of error.Thus, the effective error is 1%.

FIG. 4 e depicts the final result (FTC) 90 after activating integrationprocess on all target codes shown in FIGS. 4 a-d.

The integration process includes the following steps:

-   -   i. Comparison of all target code results generated from a        certain image with those generating from other images of the        same target code 10. For example if in one image the first        character was identified as the number 8 while in others it was        identified as the letter B the final result will show B as the        first character.    -   ii. Each character in the target code receives a mark according        to the relative accuracy of identification. As In the example        given above if the number 8 has a final mark of 40% while the        letter B has a final mark of 90% the letter B will be shown in        the final code identification results (FTC 90).    -   iii. The integration process also includes comparison of data        generated with pre set data from the program database file. If,        for example, the first character in the target code was        identified as the number 1, and according to the data in the        program file the first character is always the letter I, the        letter I will be chosen and will be shown in the final code        identification results (FTC 90).

FIG. 5 illustrates an exemplary embodiment of Image capturing unit 110from the side and from the front.

Image capturing unit 110 includes the following parts:

camera 112, illumination unit 115, camera memory card 113 andillumination card 117.

In the following example the illumination and photography compartmentsare combined, other identification systems exist in which the two aremounted on separate devices as in the port chassis identification system(TCCRS) where extra strong illumination is required.

The type of camera 112 mounting device is determined according to thecamera in use. The camera system is designed according to the specificclient and location requirements. The illumination memory card 117features four different illumination levels—low, medium, high andillumination off level.

Camera card 117 is connected to illumination card 114, this fact enablessolid-state illumination output to be synchronized to the camera scanexposure time. This method increases the signal to noise ratio, sinceenergy is used only when the exact iris opens. Illumination unit 115 isturned off most of the time, and activated only when the camera unit isactivated. This means that the illumination unit is activated only forthe duration of fractions of a second depending on the speed of thecamera, the energy required for illumination this way is only 15V and3.3 A. Much energy is saved that way, and lifetime of illumination unit115 increases and at the same time illumination unit 115 maintenancecosts are reduced. In comparison with other systems where a lightprojector is used and constantly activated during the entire operation,requires 60 A or more energy supply and usually suffers from a shortlifetime.

During recognition process 234 camera 112 and illumination 115 unitsoperate in the following way:

Image capturing unit 110 is incessantly activated

When target object 20 enters capture zone 40 recognition application 160send a command to frame grabber 132 or/and video servers 130 to saveimages of target object 20. The command includes all relevantinstructions such as the number of pictures required, the degree ofillumination and the zoom distance. At the moment of capture,illumination unit 115 provides the necessary illumination level. Thereare different spectrums used according to target object characteristics,near IR (880 mm) red (660 mm) yellow (590 mm) or other spectrums.

The image capture procedure is repeated until a stop command isreceived. In cases when OCRS 100 includes several cameras, each camerareceives an independent command and is operated separately with theappropriate illumination and zoom levels.

The different identification systems described here in FIGS. 6-14 areall based on OCRS 100 as described above in FIGS. 1-5, and all includesimilar hardware and software compartments. These systems mainly differin system architecture and configuration in regard to the number ofcameras and illumination devices, their position and type of sensors andlens being used. Each system is uniquely designed to meet specificobjects identification needs.

With reference to FIG. 6, an exemplary embodiment of a stand-aloneVehicle Access-Control System (VACS) 600 based on OCRS 100 is shown.

VACS 600 includes a Compact Car Controller 650 (CCC) installed at theentrance to a secured area or parking lot, gate 620, and sensor 640.VACS 600 identifies license plate 610 of a coming car, and automaticallyopens gate 620 for authorized vehicles 630 without the need for guard.VACS 600 is activated as follows: Sensor 640 (for example a loopdetector) indicates the presence of a car 630 and signals CCC 650 tostart a new recognition sequence. CCC 650 identifies car 630 and ifauthorized, opens gate 620.

During Recognition process CCC 650 performs the following steps:

-   -   1. captures multiple images (i.e. car 630 front);    -   2. locates the license plate 610;    -   3. Identifies the registration number of car 630;    -   4. compares it to an authorized list (which contains        registration number (e.g., “ABC123”) and optional owner details        (first and last name, e.g., “Bill Smith”)) stored on CCC 650        local database;    -   5. Opens gate if vehicle 630 is authorized.

Users can change the authorized list using cellular phone, Internet, amicro terminal (keyboard and display), or via an external computer.

In addition CCC 650 can also transmit recognized registration numberthrough RS232 serial line to a printer, a display system, or as an inputto external systems.

FIG. 7, illustrates VACS 600 architecture. Peripherals 710 are connectedvia a single serial port 715 to CCC 650. Peripherals 710 comprise:output devices 712, Micro Terminal 714, PC host 716 and GSM Terminal(Global System for Mobile Communication) 718.

Micro-Terminal 714 is a small hand-held (or wall mounted) ASCII terminaland keypad, for use with CCC 650. Micro-Terminal 714 contains a smalldisplay and a small keypad. Micro-Terminal 714 is used for management ofauthorized vehicles.

Output devices 712 include various devices connected by user to showinformation about incoming cars. For example outdoor display (displayinga personalized welcome message) or portable serial printer (printing theevents).

It is possible to interface the CCC 650 using a PC running Windows (anyof the standard operating system), or via network.

Member program supports the interface and enables end-user to performvarious function such as: “Add”—add a new member to the list,“Edit”—change the car plate number, “Delete”—delete one member from thelist, “Find”—find a member by car code first or last name.

GSM terminals 718 (i.e. M20 Terminal and SMS (Short Messages Service) ofGSM network operator) is used for remote interfacing 722, 724 with CCC718.

CCC 650 comprises: camera 112 and illumination unit 115 (as shown inFIG. 4), OCRS 100 unit (described in FIGS. 1-2). CCC 650 is connected topower supply 760, sensor 640 (dry-contact indicating vehicle presence)and gate 620.

VACS system uses a solid-state pulsed LED array to illuminate car plate.The illumination is controlled by recognition application 160, which canset the illumination to 3 levels (after the vehicle triggers the loopdetector) or turn it off to save energy (most of the time when there isno vehicle present).

The frame grabber settings 132 (i.e. contrast, brightness, gain) areeither predefined and constant, selected by a time—dependent look uptable, or automatically selected according to the measured brightness ofa previous images—implementing an automatic ‘software’ iris.

As mentioned above, standard installation is based on the assumptionsthat reflective car plates are needed to be identified. Thus, nearinfra-red (IR) illumination is used. For countries (such as Korea andBrazil) where non-reflective car plates are used a visible illuminationis activated. Additional special cases include some US states and Mexicowhere non-standard reflective plates also require a visibleillumination.

With reference to FIG. 8, there is shown an exemplary embodiment of aImage Searching and Processing System (ISPS) 800 that tracks car'splates, reads and identifies their numbers.

The system is mounted permanently on the roof and on the back of ISPSvehicle 850, which rides along the road and automatically scans parkingor passing car plates.

The identified number is displayed on the system display 810, and can betransferred to other Windows applications (via DDE message) ortransmitted to Wireless LAN, and personal cell phones 815.

Cameras 112 are mounted to capture one of the following primarysituations:

-   -   Parallel parking cars—that are parking in parallel to the road        (along the sidewalk or along the road side).    -   Perpendicular parking cars—cars that are parking on a square        angle to the side of the road.    -   Diagonally parking cars—cars that are parking in an angle to the        side of another car.    -   Passing cars—cars that pass the recognition vehicle on the side.

Although the standard recognition vehicle configuration includes dualcameras, there are other recognition vehicle configurations, whichinclude a single camera or triple cameras (two side cameras and one backcamera).

ISPS 800 constantly toggles, at a high speed rate, between cameras 112in order to detect the plates from one (or more) of the cameras. Thenumber of cameras determines the number of total views ISPS 800receives. The chances of achieving accurate identification grows if moreimages are received, but too many images will slow the operation downand will cause delays in the identification vehicle movement, forcing itto slow down.

FIG. 9 a shows an example of ISPS vehicle 850 with two cameras (frontcamera 854 and rear camera 852) mounted on its roof. Front camera 852will detect incoming cars (cars 857 and 859) which are in field of view870, while rear camera 852 will detect plates of outgoing cars (car 851)which are in field of view 880. Thus, each car is scanned twice by frontand rear cameras 854 and 852, which increases the detection capability.

FIG. 9 b Illustrates an ISPS vehicle 850 with a single camera mounted onits back bumper. The identification vehicle has only one field of viewthat can scan either passing fronts or rears of parking cars.

Unlike the systems described above ISPS 800 operates without pause,searching for license plates and reporting finds constantly rather thanshutting down between vehicles.

With reference to FIG. 10 a, an exemplary embodiment of a container coderecognition system for identifying containers on rail cars TOCRS 900(train optical container recognition system) is shown.

TOCRS 900 tracks and reads Container identification numbers that arecarried by a train in a port installation. Each TRCS 900 controls asingle rail track, with trains moving at either direction. TRCS 900detects a single level of containers. Each level includes a single ordouble container—or no container at all.

The identified numbers are displayed on TRCS display, and transferred toother Windows application (with DDE messages), or via the network. Theimage files could be also saved on disk.

When moving train and the containers that it carries enter detectionzone, sensors are activated and signal to TRCS program, via IO card,that the container is present. TRCS program starts the recognitionprocess: a sequence of images in different illumination levels arecaptured according to sensors (as was described in FIGS. 1-2).

FIG. 10 b shows a top view of camera configuration 910 according to TRCS900.

Cameras 911, 913, 917 and 919 are located at 4 corners. Side cameras 911and 919 take images of the side marking of container, while back cameras913 and 917 take images of the back/front of container.

Camera 911 is connected to illumination units 921, and 923 and camera919 is connected to illumination unit 927 and 929. Four sensors 931 arelocated at the right side of the trail and four sensors 933 are locatedat the left side of the trail.

The cameras, illumination and sensors units are mounted on twohorizontal pipes 937, and 939. Each horizontal pipe 937,939 stand on twovertical poles 941, which are reinforced to the ground to prevent anyeffect from passing containers.

TRCS 900 comprises the same software and hardware which were mentionedin FIGS. 1-2, and it is optimized for identifying railway containers.

TRCS 900 operates recognition process 234 as was described in FIG. 2.According to TRCS 900 recognition process, target object 20 is definedas container, and the target code 10 is defined as containeridentification number.

The operation of the TRCS 900 is activated according to the followingsteps:

-   -   1) when moving train and containers that it carries enter        detection zone, the sensors are activated;    -   2) Sensor signal to program via IO card that the container is        present;    -   3) Recognition application starts recognition process 234 which        includes the following steps;        -   i) a sequence of images in different illumination levels are            captured according to the sensors and predefined sequence            (the illumination level is controlled by 10 card.        -   ii) Images are sent to recognition application for container            marking identification.        -   iii) Identification results are sent to recognition            application database.    -   4) a single message is generated for each passing container. The        message includes recognition results, which contain container ID        number, and additional information (such as track/lane number        date and time).    -   5) Message is sent to client application where the message is        displayed (Additional client application could also use the        message).    -   6) A windows mechanism (such as DDE Share or DCOM) can also        spread the message through the network to remote Central        processors and databases (in the case of DDE, DDE client        application is provided as source file for simplified        integration into other applications.    -   7) Steps 3, 4 are repeated as the train is passing. If the train        backs up and forth, additional messages are generated.

With reference to FIG. 11 a, an exemplary embodiment of a Quay CraneRecognition System (QCRS) 970 for identifying containers on quay craneis shown.

QCRS 970 tracks and reads Container identification numbers, handled

By quay crane. Quay crane 972 is a machine that loads or unloadscontainers on a ship to/from trucks on pier. QCRS 970 handles variouscontainer configuration (20, 40, 45, 20/20 pairs).

QCRS 970 comprises PC 190, installed in crane controller equipment room974, an array of 6 cameras 975-980 and illumination units mounted onland side and sea side of the crane.

As shown in FIG. 11 b, QCRS 970 reads the images from 2 to 4 differentcameras, simultaneously, depending on the type of container that needsto be identified:

-   Container 981—cameras 979 and 976 are used.-   Container 983—cameras 976, 980,977 and 975 are used.-   Container 985—cameras 980 and 975 are used.

QCRS 970 recognition process is similar to that described in FIGS. 1,2.Recognition process 200 is performed once the crate has left the imagecapture zone (in this case target object defined as container, andtarget code 10 defined as container I.D).

With reference to FIG. 12, a screen shot 991 of an exemplary embodimentof a multi lane plane recognition system (PRS) 990, shows a samplecapture of an aircraft.

Recognition of identification marking (VH-EBT) 993 is displayed aboveplane image 994. History 995 of previous recognitions is displayed belowplane image 994.

PRS 991 is designed to identify standard fixed-wing aircraft markingthat appear on the vertical tail surface or the sides of the fuselage.PRS 990 sends the recognition results (i.e. plane marking, image filepath, lane number, date and time) to client application.

PRS 990 includes both hardware and software (as described in FIGS. 1-5)and can accommodate up to six separate lens per system.

Apart from the different usage of identification systems as described inFIG. 6-12, several monitor modules exist for each unique system design,which serve as support to different OCRS 100 (i.e. VACS, ISPS, TOCRSQCRS and PRS).

Monitor module is especially designed to handle multiple applicationssuch as VACS QCRS, connected via the network. Monitor module is usuallyinstalled on one or more central servers, or on one of theidentification system PCs.

The objectives of Monitor utility are:

-   -   1. Monitoring the status of OCRS 100.    -   2. summarizing the operation of OCRS 100 graphically.    -   3. enabling quick access to event log.    -   4. reporting of OCRS 100 status to external management systems.

With reference to FIG. 13 a, a screen shot 252 of an exemplaryembodiment of a Monitor module 250 main display shows status 254 of 14lanes 256 in a OCRS 100 array. The status 254 of each lane is indicatedby different status lights 258 red=error, yellow=warning andgreen=normal. (In FIG. 13 a all 14 lanes are indicate normal status i.egreen).

Additional information may also be overviewed by monitor modules 250such as event log 262 for each lane.

With reference to FIG. 13 b, a screen shot 272 of an exemplaryembodiment of an Monitor module 250 shows four recognition rate graphs274, 276, 278 and 282.

Recognition rate graph 274 describes a summary of a daily recognitionpercentage for every camera in each lane.

Recognition percentage is defined as the number of events with ‘good’recognition, divided by the total number of recognition events. ‘good’recognition means that recognition system output any result i.e. a goodindication of the quality of camera.

As shown in FIG. 13 b, recognition graph describes lane 6 recognitionpercentage verses time.

Graph 274 shows overall recognition rate (all line 6 cameras) during theentire day (100%).

graph 276—shows recognition rate of back container camera (80-100%).

Graph 278—shows right side camera recognition rate (55-85%).

Graph 282—shows left container camera recognition rate (50-90%).

With reference to FIG. 14 a, an exemplary embodiment of a truck andcontainer code recognition system 150 (TCCRS) is shown. TCCRS 150,correspondingly and automatically identifies: shipping containersidentification number on carrying truck, carrying truck license andwagon/chassis number plate, while the truck and containers are inmotion.

The identified numbers are displayed on TCCRS display, and transferredto other Windows application (with DDE messages), or via the network(with DDE share networking option). The image files could be also savedon disk.

When moving truck and the containers that it carries enter detectionzone, sensors are activated and signal to the TCCRS program, via IOcard, that the container is present. TCCRS program starts therecognition process: a sequence of images in different illuminationlevels are captured according to sensors (as was described in FIGS.1-2).

FIGS. 14 b and 14 c shows a top view of left and right TCCRS 150equipment configuration.

TCCRS 150 left side shown in FIG. 14 b includes: camera 51 mounted onvertical pole 61 and, camera 53 mounted on vertical pole 63. Connectionboxes, which include all needed power supply, are mounted on verticalpoles 61, 63 and are connected to ground wire conduits. Cameras 55,mounted on horizontal pipe 65, are connected to illumination units 57and 59 and to reflectors 67, 69 and 71. Camera 55 takes images of theside marking of container, while cameras 51 and camera 53 take images ofchassis and truck identification number.

TCCRS right side, shown in FIG. 14 c includes: camera 251 mounted onvertical pole 263 and, camera 253 mounted on vertical pole 265.Connection boxes 271, 273, are mounted on vertical poles 61, 63 and areconnected to ground wire conduits. Cameras 279 and 259, mounted onhorizontal pipe 261, and are connected to illumination units 267 and 269and to sensors 254, 255 and 257. Cameras 279 and 259 take right imagesof the side marking of container, while camera 251 and 253 take imagesof chassis and truck identification number.

TCCRS 150 comprises the same software and hardware which were mentionedin FIGS. 1-2, and it is optimized for identifying truck containers andtruck license and wagon/chassis number plate.

TCCRS 150 operates recognition process 234 as was described in FIG. 2.According to TCCRS recognition process, target object 20 is defined ascontainer, and the target code 10 is defined as container identificationnumber and truck license and wagon/chassis number plate.

Having described the invention with regard to certain specificembodiments thereof, it is to be understood that the description is notmeant as a limitation, since further modifications will now suggestthemselves to those skilled in the art, and it is intended to cover suchmodifications as fall within the scope of the appended claims.

1. A method for identifying at least one of a truck and a container,comprising: receiving one or more crane updates, the crane updatescorresponding to a current operational status of a crane; receiving oneor more data updates, the data updates having pre set data relevant toidentifying at least one of a truck identification number and acontainer identification number, the pre set data comprising one or morepre set characters, each of the one or more pre set characters having arelative position within the at least one of a truck identificationnumber and a container identification number; determining that thecontainer has been selected for a loading or an unloading operation;based on the determining: capturing at least a first image of a truckwith a first camera, the at least a first image of a truck comprising avisual representation of a truck identification number, capturing atleast a second image of a container with a second camera, the at least asecond image of a container comprising a visual representation of acontainer identification number, identifying one or more characterswithin at least one of the at least a first image and the at least asecond image, each of the one or more characters having a relativeposition within the at least one of the truck identification number ofthe at least a first image and the container identification number ofthe at least a second image, comparing at least one of the one or morecharacters with at least one pre set character that has a relativeposition comparable to the relative position of the at least one of theone or more characters, and recognizing, based on the comparison, atleast one of the truck identification number within the at least a firstimage of a truck and the container identification number within the atleast a second image of a container.
 2. The method of claim 1, furthercomprising receiving at a processing unit a signal from a sensor, thesignal being sent in response to an entry of an object into a capturezone.
 3. The method of claim 2, wherein the steps of capturing at leasta first image of a truck with a first camera and capturing at least asecond image of a container with a second camera are in response toreceipt of the signal.
 4. The method of claim 1, wherein the containeris being transported by, loaded onto, or unloaded from the truck.
 5. Themethod of claim , wherein the container is being loaded or unloaded by acrane.
 6. The method of claim 5, wherein the crane is a quay crane. 7.The method of claim 1, wherein the step of capturing at least a firstimage of a truck with a first camera comprises capturing at least afirst image of a license plate of the truck with a first camera.
 8. Themethod of claim 7, wherein the identifying step comprises identifyingone or more characters within the license plate.
 9. The method of claim7, wherein the truck identification number comprises a license platenumber.
 10. The method of claim 1, further comprising capturing at leasta third image of a chassis with a third camera.
 11. The method of claim10, further comprising: identifying one or more characters within thethird image; and recognizing a chassis identification number based onthe characters.
 12. The method of claim 11, wherein the chassisidentification number comprises a chassis plate number.
 13. The methodof claim 1, wherein the container is a shipping container.
 14. Themethod of claim 1, wherein the indentifying step comprises identifyingone or more characters within at least one of the first image and thesecond image using a recognition application.
 15. The method of claim 1,wherein at least one of the truck and the container are in motion. 16.The method of claim 1, wherein the identifying step further comprises:selecting one or more areas within at least one of the first image andthe second image that are likely to contain at least one of a containeridentification number and the truck identification number; and limitingthe identifying step to the selected area(s).
 17. A method foridentifying a container, comprising: receiving one or more craneupdates, the crane updates corresponding to a current operational statusof a crane; receiving one or more data updates, the data updates havingpre set data relevant to identifying a container identification number,the pre set data comprising one or more pre set characters each of theone or more pre set characters havin a relative osition within acontainer identification number; determining that the container has beenselected for a loading or an unloading operation; based on thedetermining: (a) capturing an image of the container with a camera, theimage of the container comprising a visual representation of a containeridentification number; (b) identifying one or more characters within thefirst image using a recognition application, each of the one or morecharacters having a relative position within the containeridentification number; (c) comparing at least one of the one or morecharacters with at least one pre set character that has a relativeposition comparable to the relative position of the at least one of theone or more characters; and (d) recognizing, based on the comparison, acontainer identification number.
 18. The method of claim 17, wherein thecontainer is being loaded or unloaded by a crane.
 19. The method ofclaim 17 wherein the container is a shipping container.
 20. The methodof claim 17, wherein the identifying step further comprises: selectingone or more areas within the image that are likely to contain thecontainer identification number; and limiting the identifying step tothe selected area(s).
 21. The method of claim 17, further comprisingreceiving at a processing unit a signal from a sensor, the signal beingsent in response to an entry of the container into a capture zone. 22.The method of claim 21, wherein the capturing step is in response toreceipt of the signal.
 23. An identification system comprising: a firstimage capturing unit comprising a first camera configured to capture atleast a first image containing a first target code, the first targetcode being on a truck; a second image capturing unit comprising a secondcamera configured to capture at least a second image containing a secondtarget code, the second target code being on a container; an I/O device;and a processing unit in communication with the I/O device andcomprising a recognition application executing therein which is incommunication with the first camera and the second camera; wherein atleast one of the recognition application and the processing unit isconfigured to: receive one or more crane updates, the crane updatescorresponding to a current operational status of a crane; receive one ormore data updates, the data updates having pre set data relevant toidentifying at least one of the first target code and the second targetcode, the pre set data comprising one or more pre set characters, eachof the one or more pre set characters having a relative position withinthe at least one of the first target code and the second target code;determine that the container has been selected for a loading or anunloading operation; based on a determination that the container hasbeen selected for a loading or an unloading operation: (a) receive atleast one of the first image and the second image; (b) identify one ormore characters within at least one of the at least a first image andthe at least a second image, each of the one or more characters having arelative position within the at least one of the first target code ofthe at least a first image and the second target code of the at least asecond image; (c) compare at least one of the one or more characterswith at least one pre set character that has a relative positioncomparable to the relative position of the at least one of the one ormore characters; and recognize at least one of a containeridentification number and a truck identification number based on thecomparison.
 24. The system of claim 23, further comprising a sensor incommunication with the I/O device and configured to send a signal to theprocessing unit via the I/O device in response to an entry of an objectinto a sensor field.
 25. The system of claim 23, wherein the containeris being transported by, loaded onto, or unloaded from the truck. 26.The system of claim 23, further comprising a crane that is configured toload the container.
 27. The system of claim 23, further comprising acrane that is configured to unload the container.
 28. The system ofclaim 23, wherein the truck identification number comprises a licenseplate number.
 29. The system of claim 23, further comprising a thirdimage capturing unit comprising a third camera configured to capture atleast a third image containing a third target code, the third targetcode being on a chassis.
 30. The system of claim 29, wherein therecognition application is further in communication with the thirdcamera, and wherein at least one of the recognition application and theprocessing unit is further configured to: receive at least the thirdimage and identify at least the third target code; and recognize atleast a chassis identification number based on the third target code.31. The system of claim 23, wherein at least one of the first targetcode and the second target code comprise alphanumeric target codes. 32.The system of claim 31, wherein the alphanumeric target codes have oneor more characters.
 33. The system of claim 23, wherein at least one ofthe recognition application and the processing unit is configured toreceive at least one of the first image and the second image during aloading or an unloading operation.
 34. A container identification systemcomprising: an image capturing unit comprising a camera configured tocapture an image containing a target code; an I/O device; and aprocessing unit in communication with the I/O device and comprising arecognition application executing therein wherein at least one of therecognition application and the processing unit is configured to: (a)receive one or more crane updates, the crane updates corresponding to acurrent operational status of a crane; and (b) receive one or more dataupdates, the data updates having pre set data relevant to identifyingthe target code, the pre set data comprising one or more pre setcharacters, each of the one or more pre set characters having a relativeposition within the target code; and wherein at least one of therecognition application and the processing unit is configured, based ona determination that a container has been selected for a loading or anunloading operation, to: receive the image; identify one or morecharacters within the image, each of the one or more characters having arelative position within the target code; compare at least one of theone or more characters with at least one pre set character that has arelative position comparable to the relative position of the at leastone of the one or more characters; and recognize a containeridentification number based on the comparison.
 35. The system of claim34, further comprising a sensor in communication with the I/O device andconfigured to send a signal to the processing unit via the I/O device inresponse to an entry of an object into a sensor field.
 36. The system ofclaim 35, wherein at least one of the recognition application and theprocessing unit is configured, in response to receipt of the signal, to:receive the image and identify the target code; and recognize anidentification number based on the target code.
 37. The method of claim1, wherein each of the at least a first image of a truck and the atleast a second image of a container comprise a plurality of images. 38.The method of claim 37, wherein each of the plurality of images arecaptured with different capture parameters.
 39. The method of claim 38,wherein the capture parameters of one of the plurality of images isselected based on the capture parameters of a previous image.